Correlations between causal effect sizes of proximal SNPs vary with functional annotations and implicate stabilizing selection.

Abstract

Causal disease effect sizes of proximal single-nucleotide polymorphisms (SNPs) are widely assumed to be independent but could be correlated. Here we introduce a new method, linkage disequilibrium SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs; LDSPEC produced robust estimates in simulations. Analyzing 70 UK Biobank diseases and traits (average N = 305,646), we detected significantly non-zero SNP-pair effect correlations (for example, -0.37 ± 0.09 for low-frequency positive linkage disequilibrium 0-100-bp SNP pairs) that decayed with distance and varied with allele frequency and linkage disequilibrium between SNPs. SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances. Consequently, SNP heritability estimates were smaller than estimates of the sum of causal effect size variances across SNPs, particularly for certain functional annotations. We recapitulated our findings via forward simulations involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.